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1. A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics

3. Residual cancer burden after neoadjuvant chemotherapy and long-term survival outcomes in breast cancer: a multicentre pooled analysis of 5161 patients.

4. Comparison of tumor‐informed and tumor‐naïve sequencing assays for ctDNA detection in breast cancer

5. The clonal architecture and tumour microenvironment of breast cancers are shaped by neoadjuvant chemotherapy

6. Tumor-associated neutrophil precursors impair homologous DNA repair and promote sensitivity to PARP-inhibition

7. Residual cancer burden after neoadjuvant chemotherapy and long-term survival outcomes in breast cancer: a multicentre pooled analysis of 5161 patients

8. Multi-omic machine learning predictor of breast cancer therapy response

9. Chemotherapy-drivende novoWnt pathway activation dictates a dynamic shift to a drug-tolerant state in breast cancer cells

10. Identification and characterisation of germline-associated genes as potential human cancer biomarkers

11. The temporal mutational and immune tumour microenvironment remodelling of HER2-negative primary breast cancers

12. DNA methylation landscapes of 1538 breast cancers reveal a replication-linked clock, epigenomic instability and cis-regulation

13. Prediction of cancer treatment response from histopathology images through imputed transcriptomics

15. Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups

19. Data from AACR Project GENIE: 100,000 Cases and Beyond

20. Supplementary Table from AACR Project GENIE: 100,000 Cases and Beyond

21. Supplementary Figure from AACR Project GENIE: 100,000 Cases and Beyond

22. A large-scale retrospective study in metastatic breast cancer patients using circulating tumor DNA and machine learning to predict treatment outcome and progression-free survival

23. Abstract 476: Predicting response to treatment in early breast cancer using dynamic integrative multi-omic profiling

24. Prediction of cancer treatment response from histopathology images through imputed transcriptomics

25. Residual Cancer Burden after neoadjuvant chemotherapy and long-term survival outcomes in breast cancer: a multi-center pooled analysis of 5161 patients

26. Multi-omic machine learning predictor of breast cancer therapy response

27. Erratum: The somatic mutation profiles of 2,433 breast cancers refine their genomic and transcriptomic landscapes

30. Abstract A51: Personalized monitoring of treatment response using Targeted Digital Sequencing of circulating tumor DNA

31. The Molecular Landscape of Asian Breast Cancers Reveals Clinically Relevant Population-Specific Differences

33. The Pfam protein families database

34. Personalized circulating tumor DNA analysis to detect residual disease after neoadjuvant therapy in breast cancer

35. The Genomic and Immune Landscapes of Lethal Metastatic Breast Cancer

36. Intersect-then-combine approach: improving the performance of somatic variant calling in whole exome sequencing data using multiple aligners and callers

37. Detection of residual disease after neoadjuvant therapy in breast cancer using personalized circulating tumor DNA analysis

38. Next Generation-Targeted Amplicon Sequencing (NG-TAS): An optimised protocol and computational pipeline for cost-effective profiling of circulating tumour DNA

39. The integrated genomic and immune landscapes of lethal metastatic breast cancer (MBC).

41. Shallow whole genome sequencing for robust copy number profiling of formalin-fixed paraffin-embedded breast cancers

42. A new model for estimating glomerular filtration rate in patients with cancer.

43. Integration of genomic, transcriptomic and proteomic data identifies two biologically distinct subtypes of invasive lobular breast cancer

44. Prognostic Value of MammaPrint(®) in Invasive Lobular Breast Cancer

45. Integration of genomic, transcriptomic and proteomic data identifies two biologically distinct subtypes of invasive lobular breast cancer

46. Prognostic Value of MammaPrint(®) in Invasive Lobular Breast Cancer

47. A Biobank of Breast Cancer Explants with Preserved Intra-tumor Heterogeneity to Screen Anticancer Compounds

48. The somatic mutation profiles of 2,433 breast cancers refine their genomic and transcriptomic landscapes

49. Integration of genomic, transcriptomic and proteomic data identifies two biologically distinct subtypes of invasive lobular breast cancer

50. Prognostic Value of MammaPrint® in Invasive Lobular Breast Cancer

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